



'''simple docstring'''

import math

import flax.linen as nn
import jax.numpy as jnp






def 				a  (     UpperCamelCase_    :    jnp.ndarray      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    float = 1      ,					UpperCamelCase_    :    float = 1      ,					UpperCamelCase_    :    float = 1.0e4      ,					UpperCamelCase_    :    bool = False      ,					UpperCamelCase_    :    float = 1.0      ,					)       ->	jnp.ndarray:
		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
		snake_case__	     =float(embedding_dim // 2			)
		snake_case__	     =math.log(max_timescale / min_timescale			) / (num_timescales - freq_shift)
		snake_case__	     =min_timescale * jnp.exp(jnp.arange(UpperCamelCase_      ,					dtype=jnp.floataa			) * -log_timescale_increment			)
		snake_case__	     =jnp.expand_dims(UpperCamelCase_      ,					1			) * jnp.expand_dims(UpperCamelCase_      ,					0			)

		# scale embeddings
		snake_case__	     =scale * emb

		if flip_sin_to_cos:
				snake_case__	     =jnp.concatenate([jnp.cos(UpperCamelCase_			), jnp.sin(UpperCamelCase_			)]      ,					axis=1			)
		else:
				snake_case__	     =jnp.concatenate([jnp.sin(UpperCamelCase_			), jnp.cos(UpperCamelCase_			)]      ,					axis=1			)
		snake_case__	     =jnp.reshape(UpperCamelCase_      ,					[jnp.shape(UpperCamelCase_			)[0], embedding_dim]			)
		return signal

class 							a__( nn.Module     ):
			a_	: int									=      3_2
			a_	: jnp.dtype									=      jnp.floataa






			@nn.compact
			def __call__(     self					,   _UpperCAmelCase						)							->							Optional[Any]:
					snake_case__	     =nn.Dense(self.time_embed_dim					,   dtype=self.dtype					,   name='linear_1'						)(_UpperCAmelCase						)
					snake_case__	     =nn.silu(_UpperCAmelCase						)
					snake_case__	     =nn.Dense(self.time_embed_dim					,   dtype=self.dtype					,   name='linear_2'						)(_UpperCAmelCase						)
					return temb





class 							a__( nn.Module     ):
			a_	: int									=      3_2
			a_	: bool									=      False
			a_	: float									=      1






			@nn.compact
			def __call__(     self					,   _UpperCAmelCase						)							->							Any:
					return get_sinusoidal_embeddings(
					    _UpperCAmelCase					,   embedding_dim=self.dim					,   flip_sin_to_cos=self.flip_sin_to_cos					,   freq_shift=self.freq_shift						)









'''simple docstring'''

from __future__ import annotations

SCREAMING_SNAKE_CASE__     :						List[Any]	=  1.6021e-19  # units = C






def 				a  (     UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					UpperCamelCase_    :    float      ,					)       ->	tuple[str, float]:
		if (conductivity, electron_conc, mobility).count(0			) != 1:
				raise ValueError('You cannot supply more or less than 2 values'			)
		elif conductivity < 0:
				raise ValueError('Conductivity cannot be negative'			)
		elif electron_conc < 0:
				raise ValueError('Electron concentration cannot be negative'			)
		elif mobility < 0:
				raise ValueError('mobility cannot be negative'			)
		elif conductivity == 0:
				return (
				    "conductivity",
				    mobility * electron_conc * ELECTRON_CHARGE,
				)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    conductivity / (mobility * ELECTRON_CHARGE),
				)
		else:
				return (
				    "mobility",
				    conductivity / (electron_conc * ELECTRON_CHARGE),
				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




